Experimental Assessment of Feature-based Lidar Odometry and Mapping

Abstract

This paper experimentally evaluates the performance of Lidar Odometry and Mapping (LOAM) algorithms based on two different features namely edges and planar surfaces. This work substitutes the LOAM current feature extraction method with novel SKIP-3D (SKeleton Interest Point 3D) which exploits the sparse Lidar point clouds obtained from 3D Lidar to extract high curvature points in the scan through single point scoring. The prominent features of the proposed method are the detection of sparse, non-uniform 3D point clouds and the ability to produce repeatable key points. Carefully excluding the occluded regions and reduced point cloud after discarding non-significant points enables faster processing. The original F-LOAM feature extractor and SKIP-3D were tested and compared in several benchmark datasets.